Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV

We present the implementation of Lazy Theta* over octomap's sparse octrees. This is available as a ROS package implemented i n c++.

The code was optimized to allow for global and local path planner and to ensure a large safety distance from both obstacles and unknown space.

Latest paper

This page relates to the implementation of the Lazy Theta* path planner as published in the Sensors journal, freely available at https://www.mdpi.com/1424-8220/19/1/174.

Open source code

https://github.com/margaridaCF/FlyingOctomap_code

Presentation at RosCon 2018

https://vimeo.com/292702342

https://youtu.be/UbR8OUqfwe0

Using this in your research?

Please let us know, as we are curious to find out how it enables other people's work or research. Additionally, please cite the paper:

Faria, M., Marín, R., Popović, M., Maza, I., & Viguria, A. (2019). Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV. Sensors, 19(1), 174. https://doi.org/10.3390/s19010174

BibTeX:

@article{Faria2019,

author = {Faria, Margarida and Mar{\'{i}}n, Ricardo and Popovi{\'{c}}, Marija and Maza, Ivan and Viguria, Antidio},

doi = {10.3390/s19010174},

issn = {1424-8220},

journal = {Sensors},

month = {jan},

number = {1},

pages = {174},

title = {{Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV}},

url = {http://www.mdpi.com/1424-8220/19/1/174},

volume = {19},

year = {2019}

}